Articles

PMRI Image Reconstruction Method Based on Virtual Coils and GRAPPA-enhanced Network

  • GAO Zhaoyao ,
  • ZHANG Zhan ,
  • HU Liangliang ,
  • XU Guangyu ,
  • ZHOU Sheng ,
  • HU Yuxin ,
  • LIN Zijie ,
  • ZHOU Chao
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  • 1. Institute of Energy, Hefei Comprehensive National Science Center (Anhui Energy Laboratory), Hefei 230031, China
    2. School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
    3. School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230000, China
    4. Hefei Xihe Superconducting Technology Company, Hefei 230031, China
    5. Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China

Received date: 2025-02-24

  Online published: 2025-04-24

Abstract

Parallel magnetic resonance imaging (PMRI) is an imaging technique that uses multiple receiver coils for undersampling. It utilizes spatial information to supplement the insufficient gradient phase encoding and reconstructs aliasing-free images with specific algorithms to accelerate the imaging process. To address the issue of overfitting or poor generalization when using high acceleration factors with a limited number of auto calibration signals (ACS) in PMRI algorithms based on specific scans, a reconstruction method based on virtual coils and GRAPPA-enhanced networks is proposed. This method expands the sample by using virtual conjugate coils and enhances the ACS using the GRAPPA algorithm for training a nonlinear deep learning network. Experimental results show that the proposed PMRI method can effectively reduce aliasing artifacts caused by insufficient reference data, significantly improving image reconstruction quality with fewer ACS and higher acceleration factors.

Cite this article

GAO Zhaoyao , ZHANG Zhan , HU Liangliang , XU Guangyu , ZHOU Sheng , HU Yuxin , LIN Zijie , ZHOU Chao . PMRI Image Reconstruction Method Based on Virtual Coils and GRAPPA-enhanced Network[J]. Chinese Journal of Magnetic Resonance, 2025 , 42(4) : 390 -401 . DOI: 10.11938/cjmr20253147

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